Data Connectors and Data Apps in the Data Space
Web Content on Data Connectors and Data Apps in the Data Space
AIR Institute
Department of the Environment, Housing, and Land Use Planning (Regional Government of Castile and León)
Interoperable, connected, and ready-to-use forestry data
What is it and what is it used for?
Data Connectors integrate forestry information from databases, mapping services, APIs, documents, spreadsheets, sensors, and other sources into the RETECHFOR data space. Building on this data, Data Apps function as reusable processing modules: they can transform formats and models, combine datasets, anonymize sensitive information, validate data quality, semantically enrich the data, and perform inferences using analytical or artificial intelligence models. This results in structured, interoperable data without compromising governance, security, or the traceability of the exchange.
At RETECHFOR, these capabilities are applied, for example, to the automation of data feeds from the Castilla y León Forestry Data Portal (PDFCyL) and to the preparation of information for systems such as fire detection systems. A Data App can gather meteorological, satellite, cartographic, and climatological data, verify its consistency, and prepare it for calculating geolocated risk indices or feeding smoke detection models and decision-support systems.
The problem it solves
Forestry information is scattered across agencies, platforms, and systems and is stored in databases, mapping services, documents, spreadsheets, geographic files, sensors, or APIs with incompatible structures. The challenge is not just to connect this information; it is also necessary to combine it, clean it, protect sensitive data, verify its quality, and prepare it for analysis. Connectors and Data Apps provide a common mechanism for performing these processes within the data space and delivering reliable, interoperable, and traceable information to services such as PDFCyL and other RETECHFOR use cases.
“We connect, transform, and enrich forestry data to turn it into reliable information that helps anticipate risks and make better decisions.”
What makes it innovative?
Connectivity, intelligent processing, and sovereign data sharing within a single data space
The solution combines reusable connectors for Oracle and PostgreSQL/PostGIS databases with connectors for WFS and WMS geographic services, allowing users to incorporate structured information, spatial data, maps, and digital services without having to create a separate integration for each source. On top of that layer, Data Apps bring computing power directly to the data flow: they download and extract information, cross-reference multiple sources, normalize formats, validate quality rules, anonymize sensitive fields, semantically enrich the results, and can run inference models.
The outputs are generated in uniform structures and, where appropriate, in formats such as JSON-LD. Examples include the automated updating of PDFCyL datasets, which can transform information from multiple sources into the format required by other systems.
In fire-related use cases, these data flows can combine satellite and meteorological observations with climatological data to characterize geolocated risk and provide inputs for automated smoke detection models. The data space governs the entire process through access and traceability policies, so that each organization can share and process data without losing control over it.
Expected impact
The solution will reduce the time and cost of integrating new data sources and preparing the data for use, thereby avoiding isolated development efforts for each system, format, or use case. Automating the transformation, combination, anonymization, validation, and inference of data will improve the quality and timeliness of the information used by the project’s platforms. In the context of wildfires, it will enable users to utilize tools that work with consistent meteorological, satellite, climatological, and geospatial data, thereby supporting risk characterization, early warning, and decision-making. The architecture can be expanded with new data sources, connectors, rules, and models, thereby consolidating a reusable technological foundation within RETECHFOR.
